Fast Information-theoretic Bayesian Optimisation
Authors: Binxin Ru, Michael A. Osborne, Mark Mcleod, Diego Granziol
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically that FITBO inherits the performance associated with informationtheoretic Bayesian optimisation, while being even faster than simpler Bayesian optimisation approaches, such as Expected Improvement. We conduct a series of experiments to test the empirical performance of FITBO and compare it with other popular acquisition functions. |
| Researcher Affiliation | Collaboration | 1Department of Engineering Science, University of Oxford, Oxford, UK 2Mind Foundry Ltd., Oxford. |
| Pseudocode | Yes | Algorithm 1 FITBO acquisition function |
| Open Source Code | Yes | Our Matlab code for FITBO will be available at https: //github.com/rubinxin/FITBO. |
| Open Datasets | Yes | We perform optimisation tasks on three challenging benchmark functions: Branin (defined in [0, 1]2), Eggholder (defined in [0, 1]2) and Hartmann (defined in [0, 1]6)...Boston housing dataset (Bache and Lichman, 2013)...validation set of the MNIST dataset (Le Cun et al., 1998)...breast cancer dataset (Bache and Lichman, 2013). |
| Dataset Splits | Yes | The dataset is randomly partitioned into train/validation/test sets...We compute the median IR and the median L 2 over 40 random initialisations. |
| Hardware Specification | Yes | All the timing tests were performed exclusively on a 2.3 GHz Intel Core i5. |
| Software Dependencies | No | The paper mentions 'Matlab code' but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | In all tests, we set the observation noise to σ2 n = 10 3 and resample all the hyperparameters after each function evaluation. We initialise all Bayesian optimisation algorithms with 3 random observation data and set the observation noise to σ2 n = 10 3. All experiments are repeated 40 times. |